CN111105156B - Highway road side safety risk evaluation method based on evidence reasoning - Google Patents
Highway road side safety risk evaluation method based on evidence reasoning Download PDFInfo
- Publication number
- CN111105156B CN111105156B CN201911313799.9A CN201911313799A CN111105156B CN 111105156 B CN111105156 B CN 111105156B CN 201911313799 A CN201911313799 A CN 201911313799A CN 111105156 B CN111105156 B CN 111105156B
- Authority
- CN
- China
- Prior art keywords
- evaluation
- road
- road side
- risk
- safety risk
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000011156 evaluation Methods 0.000 title claims abstract description 126
- 238000000034 method Methods 0.000 claims abstract description 18
- 239000011159 matrix material Substances 0.000 claims description 12
- 238000004364 calculation method Methods 0.000 claims description 9
- 238000013210 evaluation model Methods 0.000 claims description 5
- 241001051053 Garcinia cowa Species 0.000 claims description 4
- 230000004888 barrier function Effects 0.000 claims description 3
- 230000015572 biosynthetic process Effects 0.000 claims description 3
- 238000003786 synthesis reaction Methods 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 2
- 238000012502 risk assessment Methods 0.000 claims description 2
- 206010039203 Road traffic accident Diseases 0.000 abstract description 9
- 238000013461 design Methods 0.000 abstract description 2
- 238000004458 analytical method Methods 0.000 description 3
- 238000007405 data analysis Methods 0.000 description 3
- 230000006978 adaptation Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000011835 investigation Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 208000027418 Wounds and injury Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 230000006378 damage Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 208000014674 injury Diseases 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000013077 scoring method Methods 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0133—Traffic data processing for classifying traffic situation
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Game Theory and Decision Science (AREA)
- Theoretical Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
Compared with other evaluation methods, the road side safety risk evaluation method based on evidence reasoning can overcome subjectivity and uncertainty problems in road side safety risk evaluation under the condition of no historical traffic accident data, realizes effective evaluation of road side safety risk under the condition of uncertain information, and provides a setting basis of security facilities for newly built roads and modified roads. The method is simple and easy to operate, the obtained road side safety risk level is evaluated, the road side safety risk level can be used as the basis of road safety design, the use of safety engineering funds is optimized, and the engineering cost is saved.
Description
Technical Field
The invention belongs to the field of highway safety evaluation, and particularly relates to a highway roadside safety risk evaluation method based on evidence reasoning, wherein the method comprises the steps of index weighting, index distributed evaluation, analytical evidence reasoning algorithm, utility function and the like.
Background
In the existing road side safety evaluation research, the main adopted methods include accident data analysis, bayesian, object element analysis, set analysis, entropy weight method and other evaluation methods. The accident data analysis method requires detail of accident data, including accident type, accident position, accident cause, accident severity, accident object, traffic environment when accident happens, etc. And then researching the relation between the road side characteristic factors and the accident severity by a data analysis method and a mathematical model, and evaluating and predicting the safety risk of the road side. The method has high dependence on historical traffic accident data, and the reliability of an evaluation model and an evaluation result depends on a large amount of accident data. However, traffic accident data of most areas in China are difficult to obtain at the present stage, so that the operability of the method is weakened, and the method is not easy to popularize and apply. In order to overcome the defect of the loss of the traffic accident data, in other evaluation methods, most of evaluation experts are considered to evaluate specific risk index values according to the described index conditions, but uncertainty generated by subjective judgment is unavoidable, namely, the problem of uncertain information in risk evaluation cannot be processed.
Disclosure of Invention
In order to overcome the limitation of the road side safety evaluation at the current stage, the invention provides a road side safety risk evaluation method based on evidence reasoning. Under the condition of no historical traffic accident data, the subjectivity and uncertainty problems in road side safety risk evaluation are overcome, the effective evaluation of road side safety risk on the condition of uncertain information is realized, and the setting basis of security facilities is provided for newly built roads and reconstructed roads.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a highway roadside safety risk evaluation method based on evidence reasoning comprises the following steps:
step 1: determining K security risk factors F affecting road side security risk i (i＝1，2，…，K)，W＝(w 1 ，w 2 ，…，w K ) For the relative weights of K risk factors, the following conditions are satisfied:i=1, 2, …, K; each risk factor F i Corresponds to L i Specific indexes, the relative weight of each index is +.>The following conditions are satisfied: />w i，j ≥0，i＝1，2，…，K，j＝1，2，…，L i ；
Step 2: respectively carrying out relative weight calculation on the safety risk factors and the indexes;
step 3: for index F under the framework of distributed evaluation model by risk evaluation personnel i，j Is a distributed evaluation result S (F) i，j ) Judgment is made, g= { G 1 ，G 2 ，…，G N The N-th evaluation level, N is the number of evaluation levels, beta n，i，j As index F i，j Is rated as grade G n Confidence of (2); beta n，i，j Satisfy beta n，i，j ≥0，If->The evaluation is complete, otherwise, the evaluation is incomplete; if->The evaluation is ignored; the index evaluation result corresponding to each risk factor is represented by a distributed evaluation matrix: />i＝1，…，K，j＝1，…，L i ；
Step 4: based on distributed evaluation matrix D i Calculating a distributed evaluation result of the ith risk factor by adopting an analytical evidence reasoning algorithm;
step 5: and ordering the road side safety risks of the M road sections according to the expected safety risk degree of the road side.
Preferably, in step 1, the factors affecting road side safety risk include road side slope, road side intersection/branch, road side barrier, road side trench; road side slope factors comprise slope gradient, embankment height and slope synthesis; the road side intersection/branch factors include intersection/branch density, intersection/branch line of sight, intersection/branch angle; road side obstacle factors include lateral distance, discrete obstacles, continuous obstacles, road side net zone conditions; the road side channel factors include lateral distance, channel type.
Preferably, in step 2, the relative weights of the security risk factors and the indexes are calculated by adopting a COWA operator, and the specific steps are as follows:
(1) Factor F i The decision data set array c of (2) i ＝(c i1 ，c i2 ，…，c ie ) E is the number of experts, reorders the data from big to small, and numbers with 1 to form a new cluster series d i ＝(d i1 ，d i2 ，…，d ie )；
(2) Calculating d by using the combination number i Weighting vector is calculated:
wherein,representing the number of permutations of any f data in e-1 data;
(3) By w being F i Weighting and calculating F i Absolute weight of (2)
(4) Calculation F i Relative weight w of (2) i ：
Preferably, the specific steps of step 4 include: will distribute the evaluation matrix D i The confidence in (c) is converted into probability mass, and the specific formula is as follows:
m n，i，j ＝w i，j β n，i，j (n＝1，2，…，N，j＝1，2，…，L i ) (5)
wherein m is n，i，j Is index F i，j Upper level G n Is assigned a function, m G，i，j Representing unassigned probabilities, the value is divided into two parts:and->Wherein->The relative importance of the reaction index j +.>Incomplete evaluation of the index j is reflected; calculating the combined probability mass by adopting an analytical evidence reasoning algorithm:
wherein the ith risk factor is evaluated at a level G n Confidence of beta n I.e., the i-th risk factor is evaluated as S (F i )＝{(G n ，β n ) N=1, 2, …, N }, confidence β G Is generated by incomplete evaluation, and is required to meet
Preferably, the specific steps of step 5 include:
assuming that there are M road segments, the distributed evaluation is S (R m )＝{(G n ，β n (R m ) N=1, 2, …, N }, m=1, 2, …, M, where β n (R m ) Is the mth road segment R m Is of the evaluation grade G of (2) n Confidence of (2); introducing an expected value of the road side risk level for each evaluation level G n (n=1, 2, …, N) a metric called utility, v (G n ) Representation stands for G n Utility of (2); thus, road segment R m The expected security risk level of the road side is:
wherein beta is n (R m ) Is road segment R m Confidence G of (1) n The lower boundary of (2) and the upper boundary of (b) n (R m )+β G (R m ) Obtaining; in the evaluation level set G, G 1 And G N Is the lowest and highest evaluation level, with the lowest and highest negative utility, respectively; thus, road segment R m The maximum, minimum and average values of the road side expected security risk level are calculated as follows:
if the distributed evaluation of all indexes of the road section is complete information evaluation, beta G (R m )＝0，E(S(R m ))＝E max (R m )＝E min (R m )＝E mean (R m ) The method comprises the steps of carrying out a first treatment on the surface of the If and only if E (S (R) m ))＞E(S(R l ) Road segment R) m The expected safety risk degree of the road side is larger than that of the road section R l The method comprises the steps of carrying out a first treatment on the surface of the If partial index distributed evaluation in the road section has incomplete information evaluation, according to the maximum value sum of the expected safety risk degree of the road sideThe minimum value compares two road segments:
(1) If E min (R m )≥E max (R l ) Road segment R m Road side expected safety risk is greater than road segment R l ；
(2) If E min (R m )＝E min (R l ) And E is max (R m )＝E max (R l ) Road segment R m The road side expected safety risk is close to road segment R l ；
(3) Otherwise, the comparison is made using the following formula:
if P (R) m ＞R l ) > 0.5 road segment R m Road side expected safety risk is greater than road segment R l ；
If P (R) m ＞R l ) =0.5, then road segment R m Road side expected safety risk and road segment R l No distinction is made;
if P (R) m ＞R l ) < 0.5, road segment R m The expected safety risk of the road side is smaller than that of the road section R l 。
Preferably, in the related literature of utility values, utility values can be directly given according to the magnitude relation of evaluation grades, and the sum of the utility values is 1, the invention takes v (G n )＝{0，0.05，0.15，0.30，0.50}。
The beneficial effects are that: compared with other evaluation methods, the road side safety risk evaluation method based on evidence reasoning can overcome subjectivity and uncertainty problems in road side safety risk evaluation under the condition of no historical traffic accident data, realize effective evaluation of road side safety risk under the condition of uncertain information, and provide setting basis of security facilities for newly built roads and modified roads. The method is simple and easy to operate, the obtained road side safety risk level is evaluated, the road side safety risk level can be used as the basis of road safety design, the use of safety engineering funds is optimized, and the engineering cost is saved.
Drawings
Fig. 1 is a specific implementation step of the road side security risk evaluation method of the present invention.
Detailed Description
The following is a further description of embodiments of the invention, in conjunction with the examples.
The road side safety risk evaluation method based on evidence reasoning comprises the following steps:
1 road side safety risk evaluation index and weight
1.1 Highway roadside safety risk evaluation index system
And four factors affecting the road side safety risk of the highway are determined by comprehensively considering various possible factors causing traffic accidents on the road side of the highway, wherein the four factors comprise road side slopes, road side intersections/branches, road side barriers and road side ditches. Road side slope factors comprise slope gradient, embankment height and slope synthesis; the road side intersection/branch factors include intersection/branch density, intersection/branch line of sight, intersection/branch angle; road side obstacle factors include lateral distance, discrete obstacles, continuous obstacles, road side net zone conditions; the road side channel factors include lateral distance, channel type. The index is evaluated by adopting five evaluation sets of { low risk, lower risk, medium risk, higher risk and high risk }, and the corresponding evaluation grades are {1,2,3,4,5}.
1.2 road side safety evaluation index weight
The evaluation index system comprises K safety risk factors F i (i＝1，2，…，K)，W＝(w 1 ，w 2 ，…，w K ) For the relative weights of K risk factors, the following conditions are satisfied:w i 0, i=1, 2, …, K. Each risk factor F i Corresponds to L i Specific indexes, the relative weight of each index is W i ＝(w i，1 ，w i，2 ，…，w i ，L i ) The following conditions are satisfied: />w i，j ≥0，i＝1，2，…，K，j＝1，2，…，L i 。
The relative weight calculation is carried out on the safety risk factors and the indexes by adopting a COWA operator, and the specific steps are as follows:
(1) Factor F i Is a decision data set array (relative importance assessed by expert) c i ＝(c i1 ，c i2 ，…，c ie ) E is the number of experts, reorders the data from big to small, and numbers with 1 to form a new cluster series d i ＝(d n ，d i2 ，…，d ie )。
(2) Calculating d by using the combination number i Weighting vector is calculated:
wherein,the number of permutations and combinations of any f data among e-1 data is represented.
(3) By w being F i Weighting and calculating F i Absolute weight of (2)
(4) Calculation F i Relative weight w of (2) i ：
2 highway roadside safety risk evaluation model
2.1 index evaluation method
Each index is judged by a risk assessment person under a distributed assessment model framework, and the distributed assessment model is as follows:
S(F i，j )＝{(G n ，β n，i，j )，n＝1，2，…，N}，i＝1，2，…，K，j＝1，2，…，L i (4)
wherein S (F i，j ) Is the index F i，j G= { G 1 ，G 2 ，…，G N The N-th evaluation level, N is the number of evaluation levels, beta n，i，j As index F i，j Is rated as grade G n Is a confidence level of (2). Beta n，ij Satisfy beta n，i，j ≥0，If it isThe evaluation is complete, otherwise, the evaluation is incomplete. If->The evaluation is ignored. The index evaluation result corresponding to each risk factor is represented by a distributed evaluation matrix: />i＝1，…，K，j＝1，…，L i . Each index information in the matrix is distribution information, and can be aggregated by adopting a evidence reasoning algorithm.
2.2 evidence reasoning algorithm
Based on distributed evaluation matrix D i And (3) calculating a distributed evaluation result of the ith risk factor by adopting an analytical evidence reasoning algorithm. Distributed evaluation matrix D i The confidence in (c) must be converted to a probability mass as follows:
m n，i，j ＝w i，j β n，i，j (n＝1，2，…，N，j＝1，2，…，L i ) (5)
wherein m is n，i，j Is index F i，j Upper level G n Is assigned a function. m is m G，i，j Indicating unassigned probabilities. This value can be divided into two parts:and->Wherein->The relative importance of the reactive index j, +.>Incomplete assessment of index j is reflected. Calculating the combined probability mass by adopting an analytical evidence reasoning algorithm:
wherein the ith index is evaluated to obtain an evaluation level G n Confidence of beta n I.e. the i-th index is evaluated as S (F i )＝{(G n ，β n ) N=1, 2, …, N }. Confidence beta G Is generated by incomplete evaluation, and is required to meet
2.3 Highway roadside safety Risk ordering
Assuming that there are M road segments, the distributed evaluation is S (R m )＝{(G n ，β n (R m ) N=1, 2, …, N }, m=1, 2, …, M, where β n (R m ) Is the mth road segment R m Is of the evaluation grade G of (2) n Is a confidence level of (2). Distributed evaluation provides roadside security risk information, but is not directly applicable to analysis and sequencing of roadside security risks. In order to compare M road sections and determine their priority, a desired value of the road side risk level is introduced for each evaluation level G n (n=1, 2, …, N) a metric called utility, v (G n ) Representation stands for G n Is effective in the present invention. Thus, road segment R m Road side of (a)The expected security risk level is:
wherein beta is n (R m ) Is road segment R m Confidence G of (1) n The lower boundary of (2) and the upper boundary of (b) n (R m )+β G (R m ) Obtained. Thus G n A range of confidence levels are provided for the incomplete evaluation conditions. In the evaluation level set G, G 1 And G N Is the lowest and highest rating with the lowest and highest negative utility, respectively. Thus, R is m The maximum, minimum and average values of the expected safety risk degree of the road side of the road section are calculated as follows:
if the distributed evaluation of all indexes of the road section is complete information evaluation, beta G (R m )＝0，E(S(R m ))＝E max (R m )＝E min (R m )＝E mean (R m ). If and only if E (S (R) m ))＞E(S(R l ) Road segment R) m The expected safety risk degree of the road side is larger than that of the road section R l . If partial index distributed evaluation in the road sections has incomplete information evaluation, comparing the two road sections according to the maximum value and the minimum value of the expected safety risk degree of the road side:
(1) If E min (R m )≥E max (R l ) Road segment R m Road side anticipation arrangement of (1)The total risk is greater than the road section R l ；
(2) If E min (R m )＝E min (R l ) And E is max (R m )＝E max (R l ) Road segment R m The road side expected safety risk is close to road segment R l ；
(3) In other cases, the comparison may be made using the following formula:
if P (R) m ＞R l ) > 0.5 road segment R m Road side expected safety risk is greater than road segment R l ；
If P (R) m ＞R l ) =0.5, then road segment R m Road side expected safety risk and road segment R l No distinction is made;
if P (R) m ＞R l ) < 0.5, road segment R m The expected safety risk of the road side is smaller than that of the road section R l 。
Taking a certain three-level highway as an example, selecting a road side accident high-speed road section for 1km to evaluate the road side safety risk. And 5 road sections are divided into units of 200m from a road starting point, and road side safety risk investigation and evaluation are respectively carried out.
(1) Index weight determination
And 5 experts score the relative importance of the safety risk factors and the indexes, wherein the scoring interval is 0-5, and the score is an integer multiple of 0.5, and the higher the relative importance is, the higher the score is. The following is described by way of example only of the calculation of the relative weights of risk factors, see table 1.
TABLE 1 safety risk factor relative importance scoring results
By risk factor F 1 For example, the relative weights are calculated by using a COWA operator, and the calculation steps are as follows:
will F 1 Ranking the importance scores of (2) from big to small to obtain d 1 = (4.0,3.5,3.5,3.5,3.0). The number of experts is e=5, and a weight vector (0.0625,0.2500,0.3750,0.2500,0.0625) is calculated from equation (1). Then calculate F from (2) 1 The absolute weights of (2) are:
in the same way, the method can be used for preparing the composite material,the relative weight of the risk factor w= (0.236,0.293,0.252,0.220) is calculated from equation (3). Similarly, the relative weight W of the index 1 ＝(0.322，0.401，0.277)，W 2 ＝(0.395，0.321，0.284)，W 3 ＝(0.345，0.179，0.202，0.274)，W 4 ＝(0.578，0.422)。
(2) Distributed evaluation results
And carrying out distributed evaluation on each index of 5 road sections by a security risk investigation personnel according to the security risk index evaluation set and the distributed evaluation model, wherein the table 2 is shown in the specification.
Table 2 safety risk distributed evaluation results of evaluation index
The results of the distributed evaluation grades of 5 road segments are calculated by the formulas (5) to (15), as shown in table 3. Wherein road segments 1,4 and 5 produce a confidence level beta due to the presence of incomplete information assessment G . The distributed evaluation level results reflect the probability of 5 evaluation levels to which each road segment belongs.
Table 3 road side safety risk distribution type evaluation level results and safety risk expected values for each road section
(3) Comprehensive evaluation results
Value set v (G) n ) = {0,0.05,0.15,0.30,0.50}, the maximum value, minimum value and average value of the 5 road-section road-side safety risk expected values are calculated by equations (16) to (19), respectively, as shown in table 3. The road sections 1,4 and 5 have incomplete information evaluation, the average value, the maximum value and the minimum value of the expected safety risk value are different, and the road sections 2 and 3 are all complete information evaluation, and the three are equal. Then, the 5 road sections are subjected to comparison and sorting, and the road sections with complete information evaluation are directly compared with E mean The road segments for which incomplete information evaluation is based on the maximum and minimum of the expected value of the security risk are compared, otherwise according to equation (20). The relative security risk matrix is calculated from equation (20), see table 4. Among the road segments 1,2 and 5, the sorting is required according to the judgment condition of the formula (20). By combining tables 3 and 4, a safety risk ranking of 5 road segments can be obtained, see table 5.
TABLE 4 road segment relative safety risk matrix
(4) Verification of evaluation Effect
To verify the evaluation effect, road side traffic accident data during three years of the evaluation section is adopted for verification. The accident types are classified into three types of property loss accidents, injury accidents and death accidents, and the safety risk ranking is carried out by adopting a scoring method, namely, the three types of traffic accident types are respectively and correspondingly valued at 1,2 and 3, the safety risk scoring based on the accident data is reflected by the sum of the products of the accident times and the corresponding scores, and the ranking is carried out, and is shown in a table 5. And verifying the correlation between the road side security risk evaluation ranking based on evidence reasoning and the evaluation ranking based on accident data by adopting a Spearman rank correlation analysis method. The calculation shows that the Spearman correlation coefficient is 0.900, the P-value is 0.037, and the reliability of the road side safety risk evaluation method based on evidence reasoning is higher through the significance test with the confidence coefficient of 95 percent, namely, the model evaluation ordering and the accident data ordering have no significant difference.
Table 5 comparison of model evaluation ranking and accident data ranking
The foregoing is only a preferred embodiment of the invention, it being noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.
Claims (3)
1. The road side safety risk evaluation method based on evidence reasoning is characterized by comprising the following steps:
step 1: determining K security risk factors F affecting road side security risk i ，W＝(w 1 ,w 2 ,…,w K ) For the relative weights of K risk factors, the following conditions are satisfied:each risk factor F i Corresponds to L i Specific indexes, the relative weight of each index is +.>The following conditions are satisfied: />w i,j ≥0，i＝1,2,…,K,j＝1,2,…,L i ；
Step 2: for index F under the framework of distributed evaluation model by risk evaluation personnel i,j Is a distributed evaluation result S (F) i )＝{(G n ,β n ) N=1, 2, …, N } makes a judgment, g= { G 1 ,G 2 ,…,G N The N-th evaluation level, N is the number of evaluation levels, beta n,i,j As index F i,j Is rated as grade G n Confidence of (2); the index evaluation result corresponding to each risk factor is represented by a distributed evaluation matrix:
step 3: based on distributed evaluation matrix D i Calculating a distributed evaluation result of the ith risk factor by adopting an analytical evidence reasoning algorithm;
step 4: sorting the road side safety risks of the M road sections according to the expected safety risk degree of the road side;
in the step 1, the relative weight calculation is carried out on the security risk factors by adopting a COWA operator, and the specific steps are as follows:
(1) Risk factor F by e experts i Scoring the relative importance of the number of pairs to obtain a relative importance series c i ＝(c i1 ,c i2 ,…,c ie ) Reorder the relative importance columns from big to small data and form a new cluster column d numbered with 1 i ＝(d i1 ,d i2 ,…,d ie )；
(2) Calculating d by using the combination number i Weighting vector is calculated:
wherein,represented in e-1 data arbitrarilyf number of data arranged and combined;
(3) By w being F i Weighting and calculating F i Absolute weight of (2)
(4) Calculation F i Relative weight w of (2) i ：
In step 2, beta n,i,j Satisfy the following requirementsIf->The evaluation is complete, otherwise, the evaluation is incomplete; if->The evaluation is ignored;
the specific steps of the step 3 comprise:
step 3.1: will distribute the evaluation matrix D i The confidence in (c) is converted into probability mass, and the specific formula is as follows:
m n,i,j ＝w i,j β n,i,j (5)
wherein m is n,i,j Is index F i,j Upper level G n Is assigned a function, m G,i,j Representing unassigned probabilities, the value is divided into two parts:and->Wherein->The relative importance of the reaction index j +.>Incomplete evaluation of the index j is reflected;
step 3.2: calculating the combined probability mass by adopting an analytical evidence reasoning algorithm:
wherein the ith risk factor is evaluated at a level G n Confidence of beta n I.e., the i-th risk factor is evaluated as S (F i )＝{(G n ,β n ) N=1, 2, …, N }, confidence β G Is generated by incomplete evaluation, and is required to meet
The specific steps of the step 4 comprise:
assuming that there are M road segments, the distributed evaluation is S (R m )＝{(G n ,β n (R m ) N=1, 2, …, N }, m=1, 2, …, M, where β n (R m ) Is the mth road segment R m Is of the evaluation grade G of (2) n Confidence of (2); introducing an expected value of the road side risk level for each evaluation level G n Assigning a measure, called utility, using v (G n ) Representation stands for G n Utility of (2); thus, road segment R m The expected security risk level of the road side is:
wherein beta is n (R m ) Is road segment R m Confidence G of (1) n Lower boundary of (1) which isThe upper boundary is defined by beta n (R m )+β G (R m ) Obtaining; in the evaluation level set G, G 1 And G N Is the lowest and highest evaluation level, with the lowest and highest negative utility, respectively; thus, road segment R m The maximum, minimum and average values of the road side expected security risk level are calculated as follows:
if the distributed evaluation of all indexes of the road section is complete information evaluation, beta G (R m )＝0，E(S(R m ))＝E max (R m )＝E min (R m )＝E mean (R m ) The method comprises the steps of carrying out a first treatment on the surface of the If and only if E (S (R) m ))＞E(S(R l ) Road segment R) m The expected safety risk degree of the road side is larger than that of the road section R l The method comprises the steps of carrying out a first treatment on the surface of the If partial index distributed evaluation in the road sections has incomplete information evaluation, comparing the two road sections according to the maximum value and the minimum value of the expected safety risk degree of the road side:
(1) If E min (R m )≤E max (R l ) Road segment R m Road side expected safety risk is greater than road segment R l ；
(2) If E min (R m )＝E min (R l ) And E is max (R m )＝E max (R l ) Road segment R m The road side expected safety risk is close to road segment R l ；
(3) Otherwise, the comparison is made using the following formula:
if P (R) m ＞R l ) > 0.5 road segment R m Road side expected safety risk is greater than road segment R l ；
If P (R) m ＞R l ) =0.5, then road segment R m Road side expected safety risk and road segment R l No distinction is made;
if P (R) m ＞R l ) < 0.5, road segment R m The expected safety risk of the road side is smaller than that of the road section R l 。
2. The method for evaluating the safety risk of the road side based on evidence reasoning according to claim 1, wherein in the step 1, the factors influencing the safety risk of the road side comprise a road side slope, a road side intersection/branch, a road side barrier and a road side ditch; road side slope factors comprise slope gradient, embankment height and slope synthesis; the road side intersection/branch factors include intersection/branch density, intersection/branch line of sight, intersection/branch angle; road side obstacle factors include lateral distance, discrete obstacles, continuous obstacles, road side net zone conditions; the road side channel factors include lateral distance, channel type.
3. The evidence reasoning-based highway roadside security risk assessment method as claimed in claim 1, wherein v (G n )＝{0,0.05,0.15,0.30,0.50}。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911313799.9A CN111105156B (en) | 2019-12-19 | 2019-12-19 | Highway road side safety risk evaluation method based on evidence reasoning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911313799.9A CN111105156B (en) | 2019-12-19 | 2019-12-19 | Highway road side safety risk evaluation method based on evidence reasoning |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111105156A CN111105156A (en) | 2020-05-05 |
| CN111105156B true CN111105156B (en) | 2023-12-26 |
Family
ID=70422551
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911313799.9A Active CN111105156B (en) | 2019-12-19 | 2019-12-19 | Highway road side safety risk evaluation method based on evidence reasoning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111105156B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112819360A (en) * | 2021-02-20 | 2021-05-18 | 中国公路工程咨询集团有限公司 | Highway roadside sensing equipment layout scheme evaluation method and system |
| CN113780653A (en) * | 2021-09-07 | 2021-12-10 | 上海电力大学 | Power transmission line path scheme optimization method based on cloud matter elements |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106920040A (en) * | 2017-03-01 | 2017-07-04 | 西南交通大学 | Freeway tunnel street accidents risks appraisal procedure based on Fuzzy Level Analytic Approach |
| CN107886235A (en) * | 2017-11-08 | 2018-04-06 | 重庆科技学院 | A kind of Fire risk assessment method for coupling certainty and uncertainty analysis |
| CN109671274A (en) * | 2019-01-24 | 2019-04-23 | 交通运输部公路科学研究所 | A kind of highway risk automatic evaluation method based on latent structure and fusion |
| CN109902971A (en) * | 2019-03-26 | 2019-06-18 | 南通大学 | A kind of road side security risk evaluations and stage division |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104376420A (en) * | 2014-11-20 | 2015-02-25 | 中国石油天然气股份有限公司 | Water breakthrough risk evaluation method and evaluation device for water-carrying gas reservoir gas well |
| CN107515970A (en) * | 2017-08-09 | 2017-12-26 | 中国科学院、水利部成都山地灾害与环境研究所 | A kind of method for early warning of the dangerous three-dimensional multi-point multi objective of Landslide Section power network shaft tower |
-
2019
- 2019-12-19 CN CN201911313799.9A patent/CN111105156B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106920040A (en) * | 2017-03-01 | 2017-07-04 | 西南交通大学 | Freeway tunnel street accidents risks appraisal procedure based on Fuzzy Level Analytic Approach |
| CN107886235A (en) * | 2017-11-08 | 2018-04-06 | 重庆科技学院 | A kind of Fire risk assessment method for coupling certainty and uncertainty analysis |
| CN109671274A (en) * | 2019-01-24 | 2019-04-23 | 交通运输部公路科学研究所 | A kind of highway risk automatic evaluation method based on latent structure and fusion |
| CN109902971A (en) * | 2019-03-26 | 2019-06-18 | 南通大学 | A kind of road side security risk evaluations and stage division |
Non-Patent Citations (1)
| Title |
|---|
| 汤天培 等.基于风险分级的农村公路路测安全生命防护工程方案设计.南通大学学报（自然科学版）.2018,第第17卷卷(第第17卷期),22-29. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111105156A (en) | 2020-05-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Li et al. | Application of analytic hierarchy process in network level pavement maintenance decision-making | |
| CN111507586B (en) | Navigation water area water construction safety risk early warning grading method and system | |
| CN111105156B (en) | Highway road side safety risk evaluation method based on evidence reasoning | |
| Thube | Artificial neural network (ANN) based pavement deterioration models for low volume roads in India | |
| CN113496359B (en) | Multi-combination subjective and objective uniform optimization empowerment risk evaluation method | |
| CN111737916A (en) | Road and bridge disease analysis and maintenance decision method based on big data | |
| CN114912635A (en) | Highway maintenance decision-making method and system, storage medium and electronic equipment | |
| CN106780266B (en) | Principal component contribution degree parameter-based accident hotspot internal characteristic analysis and driving guidance method | |
| CN114329748B (en) | Safety evaluation method for composite lining structure of pipe gallery | |
| KR101502539B1 (en) | Method and apparatus for obtaining optimal location and capacity of expressway service area | |
| Issa et al. | Modeling pavement condition index using cascade architecture: classical and neural network methods | |
| Wu et al. | Safety evaluation model of highway construction based on fuzzy grey theory | |
| CN110705005B (en) | Automatic driving accident test scene screening method | |
| Arabani et al. | Rough-set theory in solving road pavement management problems (case study: Ahwaz-Shush highway) | |
| CN116596346A (en) | Expressway tunnel operation safety evaluation method | |
| CN111160719A (en) | Two-network fused multi-index evaluation method | |
| Habibian et al. | Ranking of hazardous road locations in two-lane two-way rural roads with no crash record | |
| CN112686396B (en) | Pavement maintenance property selection method, medium and system based on disease quantity | |
| CN115829206A (en) | Risk assessment method for ground collapse caused by water supply and drainage pipeline | |
| CN112712284A (en) | Urban gas pipeline risk evaluation system and method based on AHP-entropy weight method | |
| CN114118688A (en) | Power grid engineering cost risk early warning method based on sequence relation analysis | |
| CN112990599B (en) | Urban emergency fire control optimization method based on emergency fire event similarity calculation | |
| CN113298409A (en) | Maintenance method based on early warning mechanism of road management and maintenance | |
| Sutradhar et al. | Assessment of pavement shoulder condition in rural roads | |
| Zhou et al. | Research on Navigation Risk Identification of Three Gorges Ship Lock Based on Improved Analytic Hierarchy Process |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CB03 | Change of inventor or designer information | ||
| CB03 | Change of inventor or designer information |
Inventor after: Tang Tianpei Inventor after: Xu Siyuan Inventor after: Tao Yangchen Inventor after: Wang Haibo Inventor after: Shen Qiangru Inventor after: Zhu Senlai Inventor after: Cao Zhichao Inventor after: Cao Yang Inventor after: Xu Ting Inventor before: Xu Siyuan Inventor before: Tang Tianpei Inventor before: Tao Yangchen Inventor before: Wang Haibo Inventor before: Shen Qiangru Inventor before: Zhu Senlai Inventor before: Cao Zhichao Inventor before: Cao Yang Inventor before: Xu Ting |